A dual-layer flat panel x-ray detector based on an engineered amorphous chalcogenide alloy for quantifying coronary artery calcium

基于工程非晶硫属化物合金的双层平板 X 射线探测器，用于量化冠状动脉钙

• 批准号: 10504769
• 负责人: Shiva Abbaszadeh
• 金额: $ 51.79万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-07 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10504769
• 关键词: Alloys; Arterial Fatty Streak; Arteries; Artificial Intelligence; Bone Density; Calcium; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Charge; Clinical; Convection; Coronary Arteriosclerosis; Coupled; Data; Decision Making; Descriptor; Detection; Development; Disease; Early Diagnosis; Early Intervention; Engineering; Event; Future; Gold; Healthcare; Heart; Heart Diseases; Image; Imaging Techniques; Imaging technology; Location; Low Dose Radiation; Manufacturer Name; Medical Imaging; Modeling; Morbidity - disease rate; Morphologic artifacts; Motion; Motivation; Optics; Osteoporosis; Patient-Focused Outcomes; Patients; Performance; Physics; Population; Predictive Factor; Preventive healthcare; Probability; Property; Radiation; Research; Research Personnel; Resolution; Resource-limited setting; Roentgen Rays; Selenium; Structure; Sum; System; Technology; Testing; Thermal Conductivity; Thoracic Radiography; Time; Training; Tuberculosis; Vascular blood supply; X-Ray Computed Tomography; X-Ray Medical Imaging; artificial intelligence algorithm; automated algorithm; base; calcification; cardiovascular risk factor; coronary artery calcification; coronary artery calcium; cost; density; design; detection sensitivity; detector; digital; early detection biomarkers; electric field; experience; heart imaging; heart motion; image guided; image processing; imager; improved; innovation; lung cancer screening; machine learning algorithm; mortality; novel; operation; population based; quantum; radiological imaging; respiratory; screening; soft tissue; standard of care; success; vibration

PROJECT ABSTRACT Heart disease is extremely prevalent, with about one in every four deaths (in the US) being attributed to heart disease. Early detection of cardiovascular events, especially before patients become symptomatic, has immense impact in preventive healthcare, reducing the morbidity and mortality associated with cardiovascular disease. Coronary artery calcification (CAC), a strong predictor for future cardiovascular events, is a component of atherosclerotic plaque buildup in the arteries that supply blood to the heart, leading to coronary artery disease (CAD). Identification of CAC is clinically important because it is used for cardiovascular risk and therapy decision making. Currently, CAC is quantified by computed tomography (CT), however, CT-based population screening is not widely utilized due to cost and radiation burden. Chest x-rays (CXR) are the most common medical imaging procedure and have higher availability than CT in low-resource settings, lower radiation dose, and higher patient throughput that could be used for screening purposes. Unfortunately, due to the lack of quantification in CXR, only qualitative descriptors are possible. The objective of this proposal is therefore to bring much-needed quantification to CXR, particularly for detecting and quantifying CAC by combining a new dual-layer x-ray detector and artificial-intelligence based image processing. The proposed dual-layer detector utilizes alloys of amorphous selenium (a-Se) that achieve favorable electro-optical properties (e.g., higher charge carrier mobilities and higher gain) compared to conventional a-Se based x-ray detectors. This technology has four major components: (1) a top layer direct convection a-Se alloys on an imaging backplane, (2) a bottom layer indirect conversion a-Se alloy with intrinsic gain on an imaging backplane coupled to a scintillator, (3) top panel and bottom panel integration into a dual-layer detector, and (4) a machine learning algorithm that enhances accuracy of the quantitative information from the dual-layer detector. The detector development leverages a mature platform from Varex Imaging, a leading manufacturer of x-ray detectors. We expect to show that the proposed system has higher spatial resolution images and higher sensitivity to detect small, high contrast features (calcifications) and to separate materials such as calcium from soft tissue. This approach will allow accurate quantification of predictive factors and will have immense impact in proactive healthcare, improving the clinical outcomes of patients, and reducing the number of deaths associated with cardiovascular disease. While our focus is on CAC, we expect this technology to broadly improve CXR for early detection of lung cancer, tuberculosis, and other diseases such as osteoporosis via quantification of bone mineral density.

项目摘要 心脏病非常普遍，在美国，大约四分之一的死亡是由心脏引起的 疾病。早期发现心血管事件，特别是在患者出现症状之前，具有巨大的意义 预防保健方面的影响，减少与心血管疾病相关的发病率和死亡率。 冠状动脉钙化(CAC)是未来心血管事件的强烈预测指标，是 动脉粥样硬化斑块在供应心脏血液的动脉中积聚，导致冠状动脉疾病 (CAD)。CAC的识别具有重要的临床意义，因为它用于心血管风险和治疗决策。 制作。目前，CAC是通过计算机断层扫描(CT)进行量化的，然而，基于CT的人群筛查 由于成本和辐射负担，没有得到广泛利用。胸部x光(Cxr)是最常见的医学成像方法。 在低资源、低辐射剂量和更多患者的情况下，该程序具有比CT更高的可用性 可用于筛选目的的吞吐量。不幸的是，由于CXR缺乏量化， 只有定性描述符是可能的。因此，这项提议目标是带来亟需的 量化到CXR，特别是通过结合新的双层X射线来检测和量化CAC 探测器和基于人工智能的图像处理。建议的双层探测器利用合金 非晶态硒(a-Se)，可获得良好的电光性能(例如，较高的载流子 迁移率和更高的增益)。这项技术有四个主要方面 组件：(1)成像底板上的顶层直接对流Ae合金，(2)底层间接对流 在耦合到闪烁体的成像底板上具有本征增益的转换a-Se合金，(3)顶板和 将底板集成到双层探测器中，以及(4)提高精度的机器学习算法 来自双层探测器的定量信息。探测器的开发利用了成熟的 该平台由领先的X射线探测器制造商VAREX成像公司提供。我们希望表明，拟议的 系统具有更高的空间分辨率图像和更高的灵敏度来检测微小的、高对比度的特征 (钙化)并将钙等物质从软组织中分离出来。这种方法将允许准确地 预测因素的量化，将对积极主动的医疗保健产生巨大影响，改善临床 改善患者的预后，减少与心血管疾病相关的死亡人数。而我们的 重点放在CAC上，我们预计这项技术将广泛提高CXR对肺癌的早期发现， 结核病和其他疾病，如骨质疏松症，通过量化骨密度。